Research progress on SiCf/SiC composites and their cladding components

SiC_f/SiC composites are promising materials for high-temperature applications, particularly as accident-tolerant fuel cladding in nuclear reactors, due to their excellent mechanical and irradiation resistance. However, further research is needed on preparation process, interface modification, and AI integration to meet the future performance demands in extreme environments. Published in AI Mater, this review highlights advancements in SiC_f/SiC composites, focusing on improvements in preparation process, material properties, and performance under extreme conditions, offering valuable insights for their future application in high-temperature nuclear systems.

Silicon carbide fiber-reinforced silicon carbide (SiC_f/SiC) composites are rapidly emerging as essential materials in high-end applications, particularly in aerospace and nuclear industries, due to their remarkable high-temperature resistance, oxidation resistance, radiation tolerance, and corrosion resistance.  In the development of fourth-generation fission reactors, SiC_f/SiC composites are considered among the most promising accident-tolerant fuel cladding materials, thanks to their exceptional stability under extreme conditions.  However, the material's complex processing and structural characteristics still present challenges for researchers.

To address these challenges, Professor Jian Xu from Dalian University of Technology, in collaboration with leading researchers from Ningbo Institute of Materials Technology and Engineering, has compiled a comprehensive review on the fabrication processes and performance of SiC_f/SiC composites. The review focuses on improving material density, mechanical properties, and irradiation stability through various advanced processes, including chemical vapor infiltration (CVI), polymer infiltration and pyrolysis (PIP), nano impregnation and transient eutectic method (NITE), and reactive melt infiltration (RMI).

“SiC_f/SiC composites are at the forefront of high-temperature material research, but enhancing their properties for long-term use in nuclear reactors requires further innovation in preparation process,” explains Professor Jian Xu. “Optimizing these processes is essential to improving the overall performance and reliability of these materials under extreme environmental conditions.”

The review also explores the mechanical properties and microstructural changes of SiC_f/SiC composites and their cladding components when exposed to high temperatures, irradiation, and corrosion, highlighting how these factors influence the materials' long-term stability in nuclear reactors. According to the researchers, understanding these changes is crucial to ensuring the safety and efficiency of next-generation nuclear power systems.

Another critical aspect of this work is the integration of numerical simulation technologies, which have become indispensable tools in predicting the service performance of SiC_f/SiC composites. By incorporating advanced AI technologies, researchers are now able to simulate and predict material behavior with greater accuracy.  “AI has the potential to revolutionize the development of SiC_f/SiC composites by enhancing design optimization and service life predictions,” says Professor Jian Xu.

While significant progress has been made in the study of SiC_f/SiC composites as next-generation nuclear fuel cladding materials, the team emphasizes that further research is needed to optimize fabrication processes, modify material interfaces, evaluate service behaviors, and integrate AI technologies to meet the increasing performance demands of future nuclear energy systems. “In-depth studies in these areas are essential to meet the increasing performance demands of future nuclear energy systems,” adds Professor Jian Xu.

This paper “ Research Progress on SiC_f/SiC Composites and Their Cladding Components” was published in AI Mater.

Shen L, Li X, Zhang Y, Yan W, Dai Y, et al. Research progress on SiC_f/SiC composites and their cladding components. AI Mater. 2025(1):0001, https://doi.org/10.55092/aimat20250001